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Poster presentation

The low-frequency part of the extracellular electric signals, the local field potentials
(LFP), carries information about dendritic processing in neuronal populations. However,
the long-range nature of electric field makes the analysis of LFP difficult, as typically
an electrode records activity of many sources. Modern multielectrodes allow for increased
spatial resolution, hence also the need for effective data analysis methods which
would allow to get more insight. These methods include Current Source Density (CSD)
analysis and source separation methods.

In [1] we have combined inverse CSD method [2] with Independent Component Analysis (ICA) [3] to decompose activity recorded in the rat forebrain on a grid of 140 positions obtaining
physiologically plausible components across a group of seven animals. The question
remains how the obtained components are connected to the activity of neuronal populations.
To study this problem we enriched the thalamocortical model [4,5] by adding the spatial information (Figure 1A) and used it to simulate the LFP generated by a single cortical column. We used the
kernel CSD method and spatio-temporal ICA to decompose the LFP measured on a regular
grid. We compared the resulting components to the activity of the twelve cortical
populations included in the model. We found that the recorded evoked activity was
dominated by two populations of pyramidal neurons, which were well separated by ICA
(Figure 1B-1G). The activity from other populations was hardly visible on top of the main two dipoles
and we were also not able to obtain them through ICA.

Acknowledgements

Supported by grants PBZ/MNiSW/07/2006/11, 5428/B/P01/2010/39 from the Polish Ministry
of Science and Higher Education and by a grant from the Polish Ministry of Regional
Development, POIG.02.03.00-00-003/09.